Improving Prediction Of Psychiatric Readmission Risk In Psychosis Patients With Natural Language Processing of Electronic Health Records

Synopsis: Psychotic disorders typically emerge in late adolescence or early adulthood, and affect approximately 2.5-4% of the population, making them one of the leading causes of disability worldwide. Up to 13% of psychiatric patients are readmitted shortly after discharge. Readmissions are disruptive for patients and families, and are a key driver of rising healthcare costs. In 2009 alone, the costs of readmissions of in psychiatric disorders had already totaled to be $832 billion.

Dr. Hall research focuses on using multimodal approaches to link patients’ neurobiological and clinical profiles with their functional recovery trajectories. The overarching goals of her research are to identify individuals with different functional recovery paths and to develop individually tailored and effective treatments. In this talk, she will present projects aiming to apply natural language processing (NLP) and machine learning techniques to extracts meaningful information from unstructured clinical narratives in patient’s electronic health records (EHR) and to create tools for evaluating and predicting psychosis patient readmission risk.